+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.maha5b.v <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv4b.v_strat", refinement.method = "mahalanobis",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3)) + I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att" , outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.ps.m5.v <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv4b.v_strat", refinement.method = "ps.match",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
pm.ps.w5.v <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv4b.v_strat", refinement.method = "ps.weight",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
pm.CBPSm5.v <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv4b.v_strat", refinement.method = "CBPS.match",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3)) + I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att" , outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.CBPSw5.v <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv4b.v_strat", refinement.method = "CBPS.weight",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.4b.no_v_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
msets.none5.v <- pm.none5.v$att
msets.maha5b.v <- pm.maha5b.v$att
msets.ps.m5.v <- pm.ps.m5.v$att
msets.ps.w5.v <- pm.ps.w5.v$att
msets.CBPSm5.v <- pm.CBPSm5.v$att
msets.CBPSw5.v <- pm.CBPSw5.v$att
pe.results5.v2_maha5b <- PanelEstimate(sets = pm.maha5b.v, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_ps.m5 <- PanelEstimate(sets = pm.ps.m5.v, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_ps.w5 <- PanelEstimate(sets = pm.ps.w5.v, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_CBPSm5 <- PanelEstimate(sets = pm.CBPSm5.v, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_CBPSw5 <- PanelEstimate(sets = pm.CBPSw5.v, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
##################
# The Results!
##################
plot(pe.results5.v2_maha5b)
plot(pe.results5.v2_ps.m5)
plot(pe.results5.v2_ps.w5)
plot(pe.results5.v2_CBPSm5)
plot(pe.results5.v2_CBPSw5)
pm.none5.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "none",
data = data1, match.missing = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3)) + I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.maha5b.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "mahalanobis",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3)) + I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att" , outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.ps.m5.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "ps.match",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
pm.ps.w5.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "ps.weight",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
pm.CBPSm5.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "CBPS.match",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3)) + I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att" , outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE,
use.diagonal.variance.matrix = TRUE)
pm.CBPSw5.s <- PanelMatch(lag = 3, time.id = "year", unit.id = "divipola",
treatment = "iv3.s_strat", refinement.method = "CBPS.weight",
data = data1, match.missing = FALSE, listwise.delete = TRUE,
covs.formula = ~ I(lag(cv1.lpop, 1:3))
+ I(lag(cv2.rur_p, 1:3))
+ I(lag(cv3c.c_lsh1, 1:3)) + I(lag(cv4.nbi, 1:3))
+ I(lag(cv5b.1.bd_o, 1:3)) + I(lag(cv6.1.ce_o_r, 1:3))
+ I(lag(cv7.3.pgf_cd_o_r, 1:3)) + I(lag(cv8.3.no_s_str, 1:3))
+ I(lag(dv4.3.reb_cdv_r, 1:3)),
size.match = 5, qoi = "att", outcome.var = "dv4.3.reb_cdv_r",
lead = 0:4, forbid.treatment.reversal = FALSE)
msets.none5.s <- pm.none5.s$att
msets.maha5b.s <- pm.maha5b.s$att
msets.ps.m5.s <- pm.ps.m5.s$att
msets.ps.w5.s <- pm.ps.w5.s$att
msets.CBPSm5.s <- pm.CBPSm5.s$att
msets.CBPSw5.s <- pm.CBPSw5.s$att
pe.results5.s2_maha5b <- PanelEstimate(sets = pm.maha5b.s, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.m5 <- PanelEstimate(sets = pm.ps.m5.s, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.w5 <- PanelEstimate(sets = pm.ps.w5.s, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSm5 <- PanelEstimate(sets = pm.CBPSm5.s, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSw5 <- PanelEstimate(sets = pm.CBPSw5.s, data = data1,
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.s2_maha5b)
plot(pe.results5.s2_ps.m5)
plot(pe.results5.s2_ps.w5)
plot(pe.results5.s2_CBPSm5)
plot(pe.results5.s2_CBPSw5)
data1 <- data1 %>%
mutate(zone2 = case_when(cv5b.1.bd_o < 0.34 ~ "Dominant",
TRUE ~ "Nondominant"))
pe.results5.p2_maha5b_mod1 <- PanelEstimate(sets = pm.maha5b.p, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.m5_mod1 <- PanelEstimate(sets = pm.ps.m5.p, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.w5_mod1 <- PanelEstimate(sets = pm.ps.w5.p, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSm5_mod1 <- PanelEstimate(sets = pm.CBPSm5.p, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSw5_mod1 <- PanelEstimate(sets = pm.CBPSw5.p, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.p2_maha5b_mod1[["Dominant"]])
plot(pe.results5.p2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.p2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.p2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.p2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.p2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.p2_CBPSm5_mod1[["Dominant"]])
plot(pe.results5.p2_CBPSm5_mod1[["Nondominant"]])
plot(pe.results5.p2_CBPSw5_mod1[["Dominant"]])
plot(pe.results5.p2_CBPSw5_mod1[["Nondominant"]])
pe.results5.s2_maha5b_mod1 <- PanelEstimate(sets = pm.maha5b.s, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.m5_mod1 <- PanelEstimate(sets = pm.ps.m5.s, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.w5_mod1 <- PanelEstimate(sets = pm.ps.w5.s, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSm5_mod1 <- PanelEstimate(sets = pm.CBPSm5.s, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSw5_mod1 <- PanelEstimate(sets = pm.CBPSw5.s, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.s2_maha5b_mod1[["Dominant"]])
plot(pe.results5.s2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.s2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.s2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.s2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.s2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.s2_CBPSm5_mod1[["Dominant"]])
plot(pe.results5.s2_CBPSm5_mod1[["Nondominant"]])
plot(pe.results5.s2_CBPSw5_mod1[["Dominant"]])
plot(pe.results5.s2_CBPSw5_mod1[["Nondominant"]])
pe.results5.v2_maha5b_mod1 <- PanelEstimate(sets = pm.maha5b.v, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_ps.m5_mod1 <- PanelEstimate(sets = pm.ps.m5.v, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_ps.w5_mod1 <- PanelEstimate(sets = pm.ps.w5.v, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_CBPSm5_mod1 <- PanelEstimate(sets = pm.CBPSm5.v, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.v2_CBPSw5_mod1 <- PanelEstimate(sets = pm.CBPSw5.v, data = data1,
moderator = "zone2",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.v2_maha5b_mod1[["Dominant"]])
plot(pe.results5.v2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.v2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.v2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.v2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.v2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.v2_CBPSm5_mod1[["Dominant"]])
plot(pe.results5.v2_CBPSm5_mod1[["Nondominant"]])
plot(pe.results5.v2_CBPSw5_mod1[["Dominant"]])
plot(pe.results5.v2_CBPSw5_mod1[["Nondominant"]])
data1 <- data1 %>%
mutate(l_supp = case_when(cv3c.c_lsh1 > median_c_lsh_m ~ "High",
cv3c.c_lsh1 < median_c_lsh_m | cv3c.c_lsh1 == 0 ~ "Low"))
pe.results5.p2_maha5b_mod2 <- PanelEstimate(sets = pm.maha5b.p, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.m5_mod2 <- PanelEstimate(sets = pm.ps.m5.p, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.w5_mod2 <- PanelEstimate(sets = pm.ps.w5.p, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSm5_mod2 <- PanelEstimate(sets = pm.CBPSm5.p, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSw5_mod2 <- PanelEstimate(sets = pm.CBPSw5.p, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.p2_maha5b_mod2[["Low"]])
plot(pe.results5.p2_maha5b_mod2[["High"]])
plot(pe.results5.p2_ps.m5_mod2[["Low"]])
plot(pe.results5.p2_ps.m5_mod2[["High"]])
plot(pe.results5.p2_ps.w5_mod2[["Low"]])
plot(pe.results5.p2_ps.w5_mod2[["High"]])
plot(pe.results5.p2_CBPSm5_mod2[["Low"]])
plot(pe.results5.p2_CBPSm5_mod2[["High"]])
plot(pe.results5.p2_CBPSw5_mod2[["Low"]])
plot(pe.results5.p2_CBPSw5_mod2[["High"]])
pe.results5.s2_maha5b_mod2 <- PanelEstimate(sets = pm.maha5b.s, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.m5_mod2 <- PanelEstimate(sets = pm.ps.m5.s, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.w5_mod2 <- PanelEstimate(sets = pm.ps.w5.s, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSm5_mod2 <- PanelEstimate(sets = pm.CBPSm5.s, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSw5_mod2 <- PanelEstimate(sets = pm.CBPSw5.s, data = data1,
moderator = "l_supp",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.s2_maha5b_mod2[["Low"]])
plot(pe.results5.s2_maha5b_mod2[["High"]])
plot(pe.results5.s2_ps.m5_mod2[["Low"]])
plot(pe.results5.s2_ps.m5_mod2[["High"]])
plot(pe.results5.s2_ps.w5_mod2[["Low"]])
plot(pe.results5.s2_ps.w5_mod2[["High"]])
plot(pe.results5.s2_CBPSm5_mod2[["Low"]])
plot(pe.results5.s2_CBPSm5_mod2[["High"]])
plot(pe.results5.s2_CBPSw5_mod2[["Low"]])
plot(pe.results5.s2_CBPSw5_mod2[["High"]])
data1 <- data1 %>%
mutate(reb_ca = case_when(reb_ru2 == 0 ~ "No Recruitment",
reb_ru2 == 1 ~ "Recruitment"))
pe.results5.p2_maha5b_mod3 <- PanelEstimate(sets = pm.maha5b.p, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.m5_mod3 <- PanelEstimate(sets = pm.ps.m5.p, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_ps.w5_mod3 <- PanelEstimate(sets = pm.ps.w5.p, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSm5_mod3 <- PanelEstimate(sets = pm.CBPSm5.p, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.p2_CBPSw5_mod3 <- PanelEstimate(sets = pm.CBPSw5.p, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.p2_maha5b_mod3[["No Recruitment"]])
plot(pe.results5.p2_maha5b_mod3[["Recruitment"]])
plot(pe.results5.p2_ps.m5_mod3[["No Recruitment"]])
plot(pe.results5.p2_ps.m5_mod3[["Recruitment"]])
plot(pe.results5.p2_ps.w5_mod3[["No Recruitment"]])
plot(pe.results5.p2_ps.w5_mod3[["Recruitment"]])
plot(pe.results5.p2_CBPSm5_mod3[["No Recruitment"]])
plot(pe.results5.p2_CBPSm5_mod3[["Recruitment"]])
plot(pe.results5.p2_CBPSw5_mod3[["No Recruitment"]])
plot(pe.results5.p2_CBPSw5_mod3[["Recruitment"]])
pe.results5.s2_maha5b_mod3 <- PanelEstimate(sets = pm.maha5b.s, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.m5_mod3 <- PanelEstimate(sets = pm.ps.m5.s, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_ps.w5_mod3 <- PanelEstimate(sets = pm.ps.w5.s, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSm5_mod3 <- PanelEstimate(sets = pm.CBPSm5.s, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
pe.results5.s2_CBPSw5_mod3 <- PanelEstimate(sets = pm.CBPSw5.s, data = data1,
moderator = "reb_ca",
se.method = "conditional",
number.iterations = 1000,
confidence.level = .95)
plot(pe.results5.s2_maha5b_mod3[["No Recruitment"]])
plot(pe.results5.s2_maha5b_mod3[["Recruitment"]])
plot(pe.results5.s2_ps.m5_mod3[["No Recruitment"]])
plot(pe.results5.s2_ps.m5_mod3[["Recruitment"]])
plot(pe.results5.s2_ps.w5_mod3[["No Recruitment"]])
plot(pe.results5.s2_ps.w5_mod3[["Recruitment"]])
plot(pe.results5.s2_CBPSm5_mod3[["No Recruitment"]])
plot(pe.results5.s2_CBPSm5_mod3[["Recruitment"]])
plot(pe.results5.s2_CBPSw5_mod3[["No Recruitment"]])
plot(pe.results5.s2_CBPSw5_mod3[["Recruitment"]])
plot(pe.results5.p2_maha5b_mod1[["Dominant"]])
plot(pe.results5.p2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.p2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.p2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.p2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.p2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.p2_CBPSm5_mod1[["Dominant"]])
plot(pe.results5.p2_CBPSm5_mod1[["Nondominant"]])
plot(pe.results5.p2_CBPSw5_mod1[["Dominant"]])
plot(pe.results5.p2_CBPSw5_mod1[["Nondominant"]])
plot(pe.results5.s2_maha5b_mod1[["Dominant"]])
plot(pe.results5.p2_maha5b_mod1[["Dominant"]])
plot(pe.results5.s2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.s2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.s2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.s2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.s2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.v2_maha5b_mod1[["Dominant"]])
plot(pe.results5.v2_maha5b_mod1[["Nondominant"]])
plot(pe.results5.v2_ps.m5_mod1[["Dominant"]])
plot(pe.results5.v2_ps.m5_mod1[["Nondominant"]])
plot(pe.results5.v2_ps.w5_mod1[["Dominant"]])
plot(pe.results5.v2_ps.w5_mod1[["Nondominant"]])
plot(pe.results5.v2_CBPSm5_mod1[["Dominant"]])
plot(pe.results5.v2_CBPSm5_mod1[["Nondominant"]])
plot(pe.results5.v2_CBPSw5_mod1[["Dominant"]])
plot(pe.results5.v2_CBPSw5_mod1[["Nondominant"]])
plot(pe.results5.p2_maha5b_mod2[["Low"]])
plot(pe.results5.p2_maha5b_mod2[["High"]])
plot(pe.results5.p2_ps.m5_mod2[["Low"]])
plot(pe.results5.p2_ps.m5_mod2[["High"]])
plot(pe.results5.p2_ps.w5_mod2[["Low"]])
plot(pe.results5.p2_ps.w5_mod2[["High"]])
plot(pe.results5.p2_CBPSm5_mod2[["Low"]])
plot(pe.results5.p2_CBPSm5_mod2[["High"]])
plot(pe.results5.p2_CBPSw5_mod2[["Low"]])
plot(pe.results5.p2_CBPSw5_mod2[["High"]])
plot(pe.results5.s2_maha5b_mod2[["Low"]])
plot(pe.results5.s2_maha5b_mod2[["High"]])
plot(pe.results5.s2_ps.m5_mod2[["Low"]])
plot(pe.results5.s2_ps.m5_mod2[["High"]])
plot(pe.results5.s2_ps.w5_mod2[["Low"]])
plot(pe.results5.s2_ps.w5_mod2[["High"]])
plot(pe.results5.s2_CBPSm5_mod2[["Low"]])
plot(pe.results5.s2_CBPSm5_mod2[["High"]])
plot(pe.results5.s2_CBPSw5_mod2[["Low"]])
plot(pe.results5.s2_CBPSw5_mod2[["High"]])
plot(pe.results5.p2_maha5b_mod3[["No Recruitment"]])
plot(pe.results5.p2_maha5b_mod3[["Recruitment"]])
plot(pe.results5.p2_ps.m5_mod3[["No Recruitment"]])
plot(pe.results5.p2_ps.m5_mod3[["Recruitment"]])
plot(pe.results5.p2_ps.w5_mod3[["No Recruitment"]])
plot(pe.results5.p2_ps.w5_mod3[["Recruitment"]])
plot(pe.results5.p2_CBPSm5_mod3[["No Recruitment"]])
plot(pe.results5.p2_CBPSm5_mod3[["Recruitment"]])
plot(pe.results5.p2_CBPSw5_mod3[["No Recruitment"]])
plot(pe.results5.p2_CBPSw5_mod3[["Recruitment"]])
plot(pe.results5.s2_maha5b_mod3[["No Recruitment"]])
plot(pe.results5.s2_maha5b_mod3[["Recruitment"]])
plot(pe.results5.s2_ps.m5_mod3[["No Recruitment"]])
plot(pe.results5.s2_ps.m5_mod3[["Recruitment"]])
plot(pe.results5.s2_ps.w5_mod3[["No Recruitment"]])
plot(pe.results5.s2_ps.w5_mod3[["Recruitment"]])
plot(pe.results5.s2_CBPSm5_mod3[["No Recruitment"]])
plot(pe.results5.s2_CBPSm5_mod3[["Recruitment"]])
plot(pe.results5.s2_CBPSw5_mod3[["No Recruitment"]])
plot(pe.results5.s2_CBPSw5_mod3[["Recruitment"]])
